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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2502.15689 |
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| _version_ | 1866909505354203136 |
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| author | Mohamed, Saher Farah, Kirollos Lotfy, Abdelrahman Rizk, Kareem Saeed, Abdelrahman Mohamed, Shahenda Khouriba, Ghada Arafa, Tamer |
| author_facet | Mohamed, Saher Farah, Kirollos Lotfy, Abdelrahman Rizk, Kareem Saeed, Abdelrahman Mohamed, Shahenda Khouriba, Ghada Arafa, Tamer |
| contents | Knowledge graphs are an efficient method for representing and connecting information across various concepts, useful in reasoning, question answering, and knowledge base completion tasks. They organize data by linking points, enabling researchers to combine diverse information sources into a single database. This interdisciplinary approach helps uncover new research questions and ideas. Knowledge graphs create a web of data points (nodes) and their connections (edges), which enhances navigation, comprehension, and utilization of data for multiple purposes. They capture complex relationships inherent in unstructured data sources, offering a semantic framework for diverse entities and their attributes. Strategies for developing knowledge graphs include using seed data, named entity recognition, and relationship extraction. These graphs enhance chatbot accuracy and include multimedia data for richer information. Creating high-quality knowledge graphs involves both automated methods and human oversight, essential for accurate and comprehensive data representation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15689 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Knowledge Graphs: The Future of Data Integration and Insightful Discovery Mohamed, Saher Farah, Kirollos Lotfy, Abdelrahman Rizk, Kareem Saeed, Abdelrahman Mohamed, Shahenda Khouriba, Ghada Arafa, Tamer Artificial Intelligence Machine Learning Knowledge graphs are an efficient method for representing and connecting information across various concepts, useful in reasoning, question answering, and knowledge base completion tasks. They organize data by linking points, enabling researchers to combine diverse information sources into a single database. This interdisciplinary approach helps uncover new research questions and ideas. Knowledge graphs create a web of data points (nodes) and their connections (edges), which enhances navigation, comprehension, and utilization of data for multiple purposes. They capture complex relationships inherent in unstructured data sources, offering a semantic framework for diverse entities and their attributes. Strategies for developing knowledge graphs include using seed data, named entity recognition, and relationship extraction. These graphs enhance chatbot accuracy and include multimedia data for richer information. Creating high-quality knowledge graphs involves both automated methods and human oversight, essential for accurate and comprehensive data representation. |
| title | Knowledge Graphs: The Future of Data Integration and Insightful Discovery |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.15689 |